Automated analysis of OCT retinal scans

ABSTRACT

The present invention is related to improved methods for analysis of images of the vitreous and/or retina and/or choroid obtained by optical coherence tomography and to methods for making diagnoses of retinal disease based on the reflectivity profiles of various vitreous and/or retinal and/or choroidal layers of the retina.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/612,827, filed Nov. 12, 2019, now allowed, which is a 371 U.S.National Phase Entry of International Application No. PCT/US2018/032211,filed May 11, 2018, which claims the benefit U.S. Provisional PatentApplication No. 62/505,393, filed May 12, 2017, each of which isincorporated herein by reference in its entirety.

FIELD OF THE INVENTION

The present invention is related to improved methods for analysis ofimages of the retina obtained by optical coherence tomography and tomethods for making diagnoses of retinal disease based on thereflectivity profiles of various retinal layers of the retina. Thepresent invention can be extended to optical coherence tomographyapplication in other medical and non-medical fields, including but notlimited to, OCT imaging of other eye related tissues and structures(cornea, tear film, anterior segment, etc.), dental tissues, thegastrointestinal tract, the cardiovascular wall, the respiratory wall,and monitoring biointegration of implanted biomaterials.

BACKGROUND OF THE INVENTION

Vision starts in the retina located at the posterior part of the eye.“Rete”, the Latin origin of its name standing for “net”, connotes withtwo important properties, a two-dimensional layer structure and amultitude of connections. The retina is composed of several heavilyinterconnected neuronal layers, each with a specific functional propertyfrom the light reception to signal processing and data reduction.

In contrast to the rather similar principal organization of the retinain layers, its topography varies substantially between mammalianspecies, presumably due to evolutionary influences of the environmentalconditions. In humans and non-human primates (NHPs), a central region ofhigh visual acuity, the macula with an associated fovea, has evolved andis located temporal to the optic nerve head. In carnivores (e.g. dog,cat) a region of increased visual acuity with a higher cone and ganglioncell density is located temporal to the optic nerve and is referred toas an area centralis but lacks a fovea. Many non-predator species (e.g.sheep, cow, horse, pig) possess a less clearly defined region ofrelatively increased acuity termed the visual streak that typicallyextends horizontally across the retina, both temporal and nasal to theoptic nerve. This configuration is believed to follow the basic visualneeds of each species, namely high-acuity vision of the horizon,low-sensitivity vision of the (bright) sky, and high-sensitivity visionof the (relatively dim) ground.

Traditionally, fundus photography and fluorescein angiography have beenused to assess macroscopic retinal structure and its changes in disease,with fine details of retinal and choroidal architecture being accessibleonly via ex vivo processes like histology and immunohistochemistry. Itwas a major breakthrough in ophthalmic diagnostics when OpticalCoherence Tomography (OCT) was first introduced as a novel tool for invivo visualization of retinal layers. The resolution of third generationmodels of OCT equipment that became available a few years later finallyturned out to be sufficient for experimental research and clinicalpractice to follow the course of disease and/or monitor the effects of atherapeutic intervention over time in individual eyes.

Recent literature suggests that, although reproducible OCT findings canbe predictably obtained from healthy individuals, variable results canoccur when retinal pathology is present due to the differences inacquisition and boundary identification between machines. See, e.g.,Sadda S R, Wu Z, Walsh A C, Richine L, Dougall J, Cortez R, LaBree L D.Errors in retinal thickness measurements obtained by optical coherencetomography. Ophthalmology. 2006 Feb. 28; 113(2):285-93. Sadda S R,Joeres S, Wu Z, Updike P, Romano P, Collins A T, Walsh A C. Errorcorrection and quantitative subanalysis of optical coherence tomographydata using computer-assisted grading. Investigative ophthalmology &visual science. 2007 Feb. 1; 48(2):839-48. Terasaki H, Shirasawa M,Yamashita T, et al. Comparison of foveal microstructure imaging withdifferent spectral domain optical coherence tomography machines.Ophthalmology. 2012; 119(11):2319-27; Branchini L, Regatieri C V,Flores-Moreno I, et al. Reproducibility of choroidal thicknessmeasurements across three spectral domain optical coherence tomographysystems. Ophthalmology. 2012; 119(1):119-23; Bressler S B, Edwards A R,Chalam K V, et al. Reproducibility of spectral-domain optical coherencetomography retinal thickness measurements and conversion to equivalenttime-domain metrics in diabetic macular edema. JAMA Ophthalmol. 2014;132(9):1113-22; and Suzuma K, Yamada Y, Liu M, et al. Comparing centralretinal thickness in diabetic macular edema measured by two differentspectral-domain optical coherence tomography devices. Japanese Journalof Ophthalmology. 2011; 55(6):620-4.

Along with variability in retinal presentations, clinicians canencounter ambiguous findings of non-glaucomatous optic neuropathies andnormal optic nerve anatomy, which can lead to false positive and falsenegative OCT results.

Accordingly, what is needed in the art are improved systems andprocesses for analyzing OCT data sets.

SUMMARY OF THE INVENTION

The present invention is related to improved methods for analysis ofimages of the retina obtained by optical coherence tomography and tomethods for making diagnoses of retinal disease based on thereflectivity profiles of various retinal layers of the retina.

In some embodiments, the present invention provides an optical coherencetomography (OCT) image analysis process comprising: visualizing an OCTimage from a scan of a patients retina on a display device, wherein theimage displays a plurality of retinal layers of the retina; indicating aportion of an edge of at least one of the vitreous and/or retinal and/orchoroidal layers with a user input device to provide a designatedretinal layer portion; via a computer processor, calculating a subject'sretinal layer reflectivity profile for the designated retinal layer bya) averaging the pixel intensity in image columns for an area from about10 to 50 pixels above and below the designated retinal layer portion toprovide a local reflectivity profile and b) calculating the best fit ofthe local reflectivity profile against each column of the OCT image toidentify the pixel location of the designated retinal layer across thePCT image; and graphically identifying the retinal layer on the OCTimage on the display device.

In some embodiments, the best fit is calculated by a cross correlationalgorithm. In some embodiments, the graphically identifying the retinallayer on the OCT image on the display device comprises overlaying asurface of the retinal layer with a computer generated line. In someembodiments, the processes further comprise identifying one or morelesions in the retinal layer with the user input device. In someembodiments, the processes further comprise associating the reflectivityprofile with a disease state of the retina. In some embodiments, theprocesses further comprise the step of tagging the patient reflectivityprofile with an information identification tag, wherein the informationidentification tag comprises information selected from the groupconsisting of name of the surface, location at the retina, diseaseindication and lesion indication. In some embodiments, the displaydevice is networked with an SD-OCT device. In some embodiments, theprocesses further comprise the step of utilizing the SD-OCT device toobtain the OCT dataset of the scan of the patient's retina.

In some embodiments, the processes further comprise transmitting thetagged patient reflectivity profile and/or OCT image to a cloud server,wherein the cloud server comprises a plurality of OCT datasets fromnormal and diseased retinas; and via a processor associated with theremote server, applying one or more machine learning algorithms toanalyze the patient reflectivity profile in relation to the plurality ofOCT datasets from normal and diseased retinas to generate one or morealgorithms a automatically segment retinal layers in an OCT image and/orassociate a disease with an automated segmentation result. In someembodiments, the algorithm facilitates displaying a refined trace of thedesignated retinal layer. In some embodiments, the processes furthercomprise the step of displaying an image with a refined trace of thedesignated retinal layer generated by the algorithm. In someembodiments, the processes further comprise the step of transmitting theimage with a refined trace of the designated retinal layer to a user.

In some embodiments, the processes further comprise the algorithmassociates the reflectivity profile of the designated retinal layer witha disease state of the retina wherein the disease state is selected fromthe group consisting of subject's participating in clinical examinationsand/or clinical trials. In some embodiments, the processes furthercomprise the step of using the algorithm to associate a disease state ornormal state with the subject's retina. In some embodiments, theprocesses further comprise transmitting information about the diseasestate or normal state of the patient retina to a user. In someembodiments, the processes further comprise obtaining multiple patientretinal images over a defined time period for a given patient, via aprocessor automatically analyzing the multiple retinal images toidentify changes in the retinal images, and displaying an image showingthe identified changes in the patient retinal images.

In some embodiments, the one or more machine learning algorithms areselected from the group exemplified by but not limited to a neuralnetwork, a decision tree, a regression model, a k-nearest neighbormodel, a partial least squares model, a support vector machine and anensemble of the models that are integrated to define a algorithm.

In some embodiments, the present invention provides an optical coherencetomography (OCT) image analysis process comprising: visualizing an OCTdataset from a scan of a patients retina on a display device, whereinthe image displays a plurality of cross-sectional retinal layers of theretina; indicating a portion of an edge of at least one of the retinallayers with a user input device to provide a designated retinal layer;at a user work station, calculating a patient reflectivity profile forthe designated retinal layer and using the reflectivity profile toidentify potential retinal locations of the designated retinal layeracross the entire image; transmitting the patient reflectivity profileto a server remote from the user work station, wherein the remote servercomprises a plurality of OCT datasets from normal and diseased retinas;via a processor associated with the remote server, applying one or moremachine learning algorithms to analyze the patient reflectivity profilein relation to the plurality of OCT datasets from normal and diseasedretinas to generate one or more algorithms a automatically segmentretinal layers in an OCT image, automatically identify lesions in one ormore retinal layers, and/or associate a disease with an automatedsegmentation or lesion identification result.

In some embodiments, the algorithm facilitates displaying a refinedtrace of the designated retinal layer. In some embodiments, theprocesses further comprise the step of displaying an image with arefined trace of the designated retinal layer generated by thealgorithm. In some embodiments, the processes further comprise the stepof transmitting the image with a refined trace of the designated retinallayer to a user.

In some embodiments, the algorithm associates the reflectivity profileof the designated retinal layer with a disease state of the retinawherein the disease state is selected from the group consisting ofAge-related macular degeneration, diabetic retinopathy, retinitispigmentosa, uveitis, central vein occlusion, and other retinaldegenerations. In some embodiments, the processes further comprise thestep of using the algorithm to associate a disease state or normal statewith the patient retina. In some embodiments, the processes furthercomprise the algorithm identifies lesions in one or more retinal layers.In some embodiments, the type and/or location of the lesion is used todiagnose a disease or the retina and/or designate a stage of severity ofa disease of the retina.

In some embodiments, the processes further comprise transmittinginformation about the disease state or normal state of the patientretina to a user. In some embodiments, the processes further comprisetransmitting information about the lesion, disease associated with thelesion, or stage of severity of retinal disease to a user. In someembodiments, the processes further comprise the step of tagging thepatient reflectivity profile with an information identification tag,wherein the information identification tag comprises informationselected from the group consisting of name of the surface, location atthe retina, disease indication and lesion indication, retinal locationrelating to known retinal landmark (e.g., fovea), age, gender, race, andanimal species (other than human).

In some embodiments, the display device is networked with an SD-OCTdevice. In some embodiments, the processes further comprise the step ofutilizing the SD-OCT device to obtain the OCT dataset of the scan of thepatient's retina. In some embodiments, the server remote from the userwork station is a cloud based server. In some embodiments, the processesfurther comprise the one or more machine learning algorithms areselected from the group exemplified by but not limited to a neuralnetwork, a decision tree, a regression model, a k-nearest neighbormodel, a partial least squares model, a support vector machine and a anensemble of the models that are integrated to define a algorithm.

In some embodiments, the processes further comprise obtaining multiplepatient retinal images over a defined time period for a given patient,via a processor automatically analyzing the multiple retinal images toidentify changes in the retinal images, and displaying an image showingthe identified changes in the patient retinal images.

In some embodiments, the present invention provides an optical coherencetomography (OCT) image analysis process comprising: at a work station,visualizing an OCT dataset from a scan of a patients retina on a displaydevice, wherein the image displays a plurality of cross-sectionalretinal layers of the retina; via a processor associated with the workstation, calculating patient reflectivity profiles for retinal layersand using the reflectivity profile to identify potential retinallocations of the designated retinal layer across the entire image; viathe processor, applying one or more algorithms to automatically segmentretinal layers in an OCT image, automatically identify lesions in one ormore retinal layers, and/or associate a disease with an automatedsegmentation or lesion identification result, wherein the one or morealgorithms are updated from a remote server performing machine learningalgorithms on a database of OCT images for normal and diseased retinas;and displaying an output selected from the group consisting of an imageof the patient retina with computer-generated traces defining one ormore layers in the image of the patient retina, an association of adisease state with the reflectivity profiles associated with one or moreretinal layers in the patient retina, an identification of lesions inone or more retinal layers, an image of one or more lesions in one ormore retinal layers, an identification of disease of the retinaassociated with one or more lesions in one or more retinal layers, adesignation of a stage of severity of a disease of the retina based onone or more identified lesions in one or more retinal layers, andcombinations thereof.

In some embodiments, the algorithm associates the reflectivity profileof the designated retinal layer with a disease state of the retinawherein the disease state is selected from the group consisting ofAge-related macular degeneration, diabetic retinopathy, retinitispigmentosa, uveitis, central vein occlusion, and other retinaldegenerations. In some embodiments, the processes further comprise thestep of using the algorithm to associate a disease state or normal statewith the patient retina. In some embodiments, the algorithm identifieslesions in one or more retinal layers. In some embodiments, theprocesses further comprise the type and/or location of the lesion isused to diagnose a disease or the retina and/or designate a stage ofseverity of a disease of the retina.

In some embodiments, the processes further comprise obtaining multiplepatient retinal images over a defined time period for a given patient,via a processor automatically analyzing the multiple retinal images toidentify changes in the retinal images, and displaying an image showingthe identified changes in the patient retinal images. In someembodiments, the multiple patient retinal images are analyzed to monitordisease progression over time. In some embodiments, the multiple patientretinal images are analyzed to monitor response to a therapeutic agentover time. In some embodiments, the therapeutic agent is selected fromthe group consisting of a small molecule drug, a biologic drug, anucleic acid, and a cell. In some embodiments, the therapeutic agent isdelivered to the patient by a method selected from the group consistingof topical application to the surface of the eye, subconjunctivalinjection, systemically (IV, oral, subcutaneous, intramuscular),electrophoresis, intravitreal injection, subretinal delivery, andsuprachoroidal delivery. In some embodiments, the therapeutic agent issoluble, insoluble in a suspension, of incorporated into a biomaterialplatform.

In some embodiments, the work station is networked with an SD-OCTdevice. In some embodiments, the processes further comprise the step ofutilizing the SD-OCT device to obtain the OCT dataset of the scan of thepatient's retina. In some embodiments, the server remote from the workstation is a cloud based server.

In some embodiments, the present invention provides an optical coherencetomography (OCT) image analysis process comprising: scanning the retinaof a patient with an SD-OCT device to provide a patient OCT dataset;transmitting the patient OCT dataset to a server remote from the SC-OCTdevice, wherein the remote server comprises a processor configured toanalyze the patient OCT dataset by one or more algorithms generated bymachine learning algorithms trained with a plurality of OCT datasetsfrom normal and diseased retinas, wherein the plurality of datasetscomprise reflectivity profiles for retinal layers within the normal anddiseased retinas; via a processor associated with the remote server,applying the one or more algorithms to identify retinal layers and/orlesions in the patient retina by their associated reflectivity profiles;and generating an output selected from the group consisting of an imageof the patient retina with computer-generated traces defining one ormore layers in the image of the patient retina, an association of adisease state with the reflectivity profiles associated with one or moreretinal layers in the patient retina, an identification of lesions inone or more retinal layers, an image of one or more lesions in one ormore retinal layers, an identification of disease of the retinaassociated with one or more lesions in one or more retinal layers, adesignation of a stage of severity of a disease of the retina based onone or more identified lesions in one or more retinal layers, andcombinations thereof.

In some embodiments, the processes further comprise transmitting theoutput selected from the group consisting of an image of the patientretina with computer-generated traces defining one or more layers in theimage of the patient retina, association of a disease state with thereflectivity profiles associated with one or more retinal layers in thepatient retina and combinations thereof to a user. In some embodiments,the processes further comprise the step of using the patient OCT datasetto further train the machine learning algorithm.

In some embodiments, the processes further comprise obtaining multiplepatient retinal images over a defined time period for a given patient,via a processor automatically analyzing the multiple retinal images toidentify changes in the retinal images, and displaying an image showingthe identified changes in the patient retinal images. In someembodiments, the multiple patient retinal images are analyzed to monitordisease progression over time. In some embodiments, the multiple patientretinal images are analyzed to monitor response to a therapeutic agentover time. In some embodiments, the therapeutic agent is selected fromthe group consisting of a small molecule drug, a biologic drug, anucleic acid, and a cell. In some embodiments, the therapeutic agent isdelivered to the patient by a method selected from the group consistingof topical application to the surface of the eye, subconjunctivalinjection, systemically (IV, oral, subcutaneous, intramuscular),electrophoresis, intravitreal injection, subretinal delivery, andsuprachoroidal delivery. In some embodiments, the therapeutic agent issoluble, insoluble in a suspension, of incorporated into a biomaterialplatform.

In some embodiments, the present invention provides a processcomprising: receiving a patient OCT dataset at a sever remote from anSD-OCT device, wherein the remote server comprises a processorconfigured to analyze the patient OCT dataset by one or more algorithmsgenerated by machine learning algorithms trained with a plurality of OCTdatasets from normal and diseased retinas, wherein the plurality ofdatasets comprise reflectivity profiles for retinal layers within thenormal and diseased retinas; via a processor associated with the remoteserver, applying the one or more algorithms to identify retinal layersand or lesions in the patient retina by their associated reflectivityprofiles; and generating an output selected from the group consisting ofan image of the patient retina with computer-generated traces definingone or more layers in the image of the patient retina, association of adisease state with the reflectivity profiles associated with one or moreretinal layers in the patient retina, an identification of lesions inone or more retinal layers, an image of one or more lesions in one ormore retinal layers, an identification of disease of the retinaassociated with one or more lesions in one or more retinal layers, adesignation of a stage of severity of a disease of the retina based onone or more identified lesions in one or more retinal layers, andcombinations thereof.

In some embodiments, the processes further comprise transmitting theoutput selected from the group consisting of an image of the patientretina with computer-generated traces defining one or more layers in theimage of the patient retina, association of a disease state with thereflectivity profiles associated with one or more retinal layers in thepatient retina and combinations thereof to a user. In some embodiments,the processes further comprise the step of using the patient OCT datasetto further train the machine learning algorithm. In some embodiments,the processes further comprise obtaining multiple patient retinal imagesover a defined time period for a given patient, via a processorautomatically analyzing the multiple retinal images to identify changesin the retinal images, and displaying an image showing the identifiedchanges in the patient retinal images. In some embodiments, the multiplepatient retinal images are analyzed to monitor disease progression overtime. In some embodiments, the multiple patient retinal images areanalyzed to monitor response to a therapeutic agent over time. In someembodiments, the therapeutic agent is selected from the group consistingof a small molecule drug, a biologic drug, a nucleic acid, and a cell.In some embodiments, the therapeutic agent is delivered to the patientby a method selected from the group consisting of topical application tothe surface of the eye, subconjunctival injection, systemically (IV,oral, subcutaneous, intramuscular), electrophoresis, intravitrealinjection, subretinal delivery, and suprachoroidal delivery. In someembodiments, the therapeutic agent is soluble, insoluble in asuspension, of incorporated into a biomaterial platform.

In some embodiments, the present invention provides a workstationcomprising: a display device configured to display a an OCT retinalimage received from an SD-OCT device; a user input device configured toenable a user to indicate a portion of an edge of at least one theretinal layer on the displayed OCT retinal image; and a computerconfigured to: receive the indicated portion of an edge of at least oneretinal layer on the displayed OCT retinal image; calculate a patientretinal layer reflectivity profile for the designated retinal layer bya) averaging the pixel intensity in image columns for an area from about10 to 50 pixels above and below the designated retinal layer portion toprovide a local reflectivity profile and b) calculating the best fit ofthe local reflectivity profile against each column of the OCT image toidentify the pixel location of the designated retinal layer across thePCT image; and graphically identify the retinal layer on the OCT imageon the display device.

In some embodiments, the present invention provides a workstationcomprising: a display device configured to display a an OCT retinalimage received from an SD-OCT device; a user input device configured toenable a user to indicate a portion of an edge of at least one theretinal layer on the displayed OCT retinal image; and a computerconfigured to: receive the indicated portion of an edge of at least oneretinal layer on the displayed OCT retinal image; calculate a patientreflectivity profile for the designated retinal layer and using thereflectivity profile to identify potential retinal locations of thedesignated retinal layer across the entire image; transmit the patientreflectivity profile to a server remote from the user computer, whereinthe remote server comprises a plurality of OCT datasets from normal anddiseased retinas, wherein the remote server is configured to apply oneor more machine learning algorithms to analyze the patient reflectivityprofile in relation to the plurality of OCT datasets from normal anddiseased retinas to generate one or more algorithms associated with thereflectivity profile for the designated retinal layer; and receive fromthe remote server an output selected from the group consisting of animage of the patient retina with computer-generated traces defining oneor more layers in the image of the patient retina, association of adisease state with the reflectivity profiles associated with one or moreretinal layers in the patient retina, an identification of lesions inone or more retinal layers, an image of one or more lesions in one ormore retinal layers, an identification of disease of the retinaassociated with one or more lesions in one or more retinal layers, adesignation of a stage of severity of a disease of the retina based onone or more identified lesions in one or more retinal layers, andcombinations thereof.

In some embodiments, the present invention provides a workstationcomprising: a display device configured to display an OCT retinal imagereceived from an SD-OCT device; and a computer configured to: transmitthe patient OCT dataset to a server remote from the SD-OCT device,wherein the remote server comprises a processor configured to analyzethe patient OCT dataset by one or more algorithms generated by machinelearning algorithms trained with a plurality of OCT datasets from normaland diseased retinas, wherein the plurality of datasets comprisereflectivity profiles for retinal layers within the normal and diseasedretinas, and wherein via the processor one or more algorithms areapplied to identify retinal layers in the patient retina by theirassociated reflectivity profiles and generate an output selected fromthe group consisting of an image of the patient retina withcomputer-generated traces defining one or more layers in the image ofthe patient retina, association of a disease state with the reflectivityprofiles associated with one or more retinal layers in the patientretina and combinations thereof; and receive from the remote server anoutput selected from the group consisting of an image of the patientretina with computer-generated traces defining one or more layers in theimage of the patient retina, association of a disease state with thereflectivity profiles associated with one or more retinal layers in thepatient retina, an identification of lesions in one or more retinallayers, an image of one or more lesions in one or more retinal layers,an identification of disease of the retina associated with one or morelesions in one or more retinal layers, a designation of a stage ofseverity of a disease of the retina based on one or more identifiedlesions in one or more retinal layers, and combinations thereof.

In some embodiments, the present invention provides a workstationcomprising: a display device configured to display an OCT retinal imagereceived from an SD-OCT device; and a computer configured to: calculatepatient reflectivity profiles for retinal layers and using thereflectivity profile to identify potential retinal locations of thedesignated retinal layer across the entire image; apply one or morealgorithms to automatically segment retinal layers in an OCT image,automatically identify lesions in one or more retinal layers, and/orassociate a disease with an automated segmentation or lesionidentification result, wherein the one or more algorithms are updatedfrom a remote server performing machine learning algorithms on adatabase of OCT images for normal and diseased retinas; and display onthe display device an output selected from the group consisting of animage of the patient retina with computer-generated traces defining oneor more layers in the image of the patient retina, an association of adisease state with the reflectivity profiles associated with one or moreretinal layers in the patient retina, an identification of lesions inone or more retinal layers, an image of one or more lesions in one ormore retinal layers, an identification of disease of the retinaassociated with one or more lesions in one or more retinal layers, adesignation of a stage of severity of a disease of the retina based onone or more identified lesions in one or more retinal layers, andcombinations thereof.

In some embodiments, the present invention provides a non-transitorycomputer readable medium comprising software or instructions whichcontrol a processor to perform the steps of any of the processesdescribed above.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a process flow diagram of a process for automated segmentationof layers in an OCT image of a retina.

FIG. 2 is an OCT image of a retina.

FIG. 3 is an OCT image of a retina where a surface of a portion of aretinal layer has been designated.

DESCRIPTION OF THE INVENTION

The present invention is related to improved methods for analysis ofimages of the retina and choroid obtained by optical coherencetomography (OCT) and to methods for making diagnoses of retinal diseasebased on the reflectivity profiles of various retinal layers of theretina. OCT provides cross-sectional images based on the reflectiveproperties of the investigated sample. See, e.g., Fujimoto J G,Brezinski M E, Tearney G J, Boppart S A, Bouma B, et al. (1995) Opticalbiopsy and imaging using optical coherence tomography Nat Med. 1(9): p.970-2. And Drexler W, Sattmann H, Hermann B, Ko T H, Stur M, et al.(2003) Enhanced visualization of macular pathology with the use ofultrahigh-resolution optical coherence tomography Arch Ophthalmol.121(5): p. 695-706.

OCT is based on low-coherence interferometry using light with broadspectral bandwidth. Reflectance signal is detected when the combinationof reflected light from the sample arm traveled the “same” opticaldistance (“same” meaning a difference of less than a coherence length)as the reflected light from the reference arm. In a layered tissue likeretina, the amount of the reflected light in tissue is determined by theoptical backscattering characteristics of the corresponding tissuelayers, and consequently the alternating intensity in reflection(consisting of peaks and troughs) demonstrates the configuration ofdifferent tissue layers in the axial direction. (Huang Y, Cideciyan A V,Papastergiou G I, Banin E, Semple-Rowland S L, Milam A H, Jacobson S G.Relation of optical coherence tomography to microanatomy in normal andrd chickens. Investigative ophthalmology & visual science. 1998 Nov. 1;39(12):2405-16.). A single measurement of the reflectivity versus depthat one specific location is called A-scan, whereas the composition of animage by alignment of several consecutive A-scans is called B-scan. Seevan Velthoven M E, Faber D J, Verbraak F D, van Leeuwen T G, de Smet M D(2007) Recent developments in optical coherence tomography for imagingthe retina Prog Retin Eye Res. 26(1): p. 57-77.

A typical B-scan shows several, often alternating bands of low and highreflectivity, as plexiform layers of the retina have a higher level ofreflectivity than nuclear layers [Jacobson S G, Cideciyan A V, Aleman TS, Pianta M J, Sumaroka A, et al. (2003) Crumbs homolog 1 (CRB1)mutations result in a thick human retina with abnormal lamination HumMol Genet. 12(9): p. 1073-8.]. However, these bands and the retinallayers associated with them vary in their extent with the topographicalposition in the retina, localized lesion alteration due to diseaseprogression, and additionally species-dependent factors in retinal andchoroidal architecture as mentioned above. So far, automatedsegmentation procedures were developed using several traditional imageanalysis approaches (e.g., Ishikawa H, Piette S, Liebmann J M, Ritch R.Detecting the inner and outer borders of the retinal nerve fiber layerusing optical coherence tomography. Graefes Arch Clin Exp Ophthalmol.2002; 240(5)362-371.). These segmentation techniques typically rely onthe known retinal structure configuration in normal human retina.Consequently, segmentation errors are occur frequently seen in diseasedretinas. Experimental quantifications based on A-scans have beenperformed in the past, but have not led to a widespread use ofrespective approaches See, e.g., Barthelmes D, Sutter F K, Kurz-Levin MM, Bosch M M, Helbig H, et al. (2006) Quantitative analysis of OCTcharacteristics in patients with achromatopsia and blue-conemonochromatism Invest Ophthalmol Vis Sci. 47(3): p. 1161-6; BarthelmesD, Gillies M C, Sutter F K (2008) Quantitative OCT analysis ofidiopathic perifoveal telangiectasia Invest Ophthalmol Vis Sci. 49(5):p. 2156-62; Mataftsi A, Schorderet D F, Chachoua L, Boussalah M, Nouri MT, et al. (2007) Novel TULP1 mutation causing leber congenital amaurosisor early onset retinal degeneration Invest Ophthalmol Vis Sci. 48(11):p. 5160-7; Jacobson S G, Aleman T S, Cideciyan A V, Sumaroka A, SchwartzS B, et al. (2009) Leber congenital amaurosis caused by Lebercilin(LCAS) mutation: retained photoreceptors adjacent to retinaldisorganization Mol Vis. 15: p. 1098-106.].

Accordingly, what is needed in the art are improved methods, systems anddevices for automating the identification of retinal layers in theretina that can be visualized in an OCT retinal scan or image and/or forusing information associated with the layers identified in an OCTretinal scan or image to make diagnoses or evaluations of the diseasestate of the retina and/or choroid and to allow lonitudtudinalmonitoring of disease progression within an individual subject (animalor human).

Accordingly, in some embodiments, the processes and systems of thepresent invention utilize an OCT imaging system to obtain an OCT datasetor scan of a subject's retina. A number of OCT imaging systems areavailable that are suitable for imaging the fundus and/or retina of theeye. For example, clinicians currently have four prominent commerciallyavailable spectral-domain (SD) OCT models to choose from: SpectralisSD-OCT (Heidelberg Engineering), 3D OCT-2000 (Topcon Medical Systems),Avanti RTVue XR (Optovue), and Cirrus HD SD-OCT 5000 (Carl ZeissMeditec). In some preferred embodiments, the SD-OCT imaging devicecaptures between 26,000 and 70,000 axial-scans per second and provide 3Dimages and improved resolution, for example, an axial resolution of 3 μmto 6 μm within tissues. The increased speed and resolution provide anenhanced ability to visualize retinal layers. OCT's ability to defineparticular layers of the retina, known as “segmentation,” as well asdepth localization in tissue, also aids in identifying points ofinterest within the scans, such as lesions.

In some preferred embodiments, the OCT imaging device is communicablycoupled to a workstation via a communications link. In variousembodiments, the imaging device sends images to the workstation via thecommunications link. The communications link may be a network thatcommunicably couples the imaging device to the workstation, or may be abus that directly couples the imaging device to the workstation. Theworkstation may include any suitable type of computing system that iscapable of processing and analyzing images according to the embodimentsdescribed herein.

In various embodiments, the workstation includes a real-time,interactive image analysis module. The real-time, interactive imageanalysis module may include any suitable types of software, firmware,and/or hardware that provide for the segmentation and quantification ofimages. Further, in some embodiments, the real-time, interactive imageanalysis module includes one or more non-transitory machine-readablestorage media that provide for the segmentation and quantification ofimages.

The workstation also includes a display. The display may be a monitor,touch screen, or the like. Information relating to the segmentation andquantification of the images may be presented to a user of theworkstation in real-time via the display. In addition, the user mayinteract with, or provide feedback to, the real-time, interactive imageanalysis module in order to direct the segmentation and quantificationprocedure. For example, the information that is displayed to the usermay be updated in real-time as the user moves a pointer or cursor acrossthe display.

In various embodiments, the user provides feedback to the real-time,interactive image analysis module through a user interface that ispresented to the user via the display. The user interface may allow theuser to control the segmentation and quantification procedure for animage by moving the pointer to positions on the display that correspondto specific locations on the image. In addition, the user interface mayallow the user to adjust the information that is presented via thedisplay. For example, the user may specify specific types ofrepresentations or specific measurements for the imaging subjectrepresented by the image that are to be presented on the display.

In some embodiments, the workstation is configured to transmit imagesobtained by the OCT imaging device, and which may in some embodiments beannotated by segmentation, quantification or tagging by a user of thework station, via a network, such as a wired or wireless communicationsnetwork, to a cloud based server at a location remote from theworkstation. The function of the cloud based server is described in moredetail below.

The systems, devices and processes of the present invention may beexplained in relation to FIGS. 1 to 3 . The block diagram of FIG. 1showing a logic flow diagram is not intended to indicate that theprocess include all of the steps in every case. Moreover, any number ofadditional or alternative steps not shown in FIG. 1 may be included inthe process, depending on the details of the specific implementation.This present invention provides a semi-automatic algorithm that does notrequire the prior knowledge of which surface to be segmented, and relieson the user to provide the initial input. This algorithm is particularlyuseful for retinal and/or choroidal OCT images because of the discretelyflat-layered architecture of these structures. In some embodiments, amachine learning subroutine is performed to allow further automation,predictive modeling and development of algorithms for automatedsegmentation of OCT images and association of a disease state with theimage and data contained therein.

Referring to FIG. 1 , the process 100 of the present invention begins atblock 105 where a 3D OCT dataset is obtained. As described above, the 3DOCT dataset is preferably an image of a patient retina obtained byscanning the patient's retina with an SD-OCT machine. FIG. 2 provides anexemplary patient OCT image of a retina showing multiple,well-structured retinal layers. Traditionally, computationalsegmentation algorithms were developed for each designated surface.These algorithms utilized image segmentation methods that do not requireuser inputs. However, these algorithms often fail due to the complexityof retinal structure in healthy and diseased eyes. In addition, if auser desired to segment a particular surface that no algorithm wasdeveloped in advance, the only option available will be to draw thedesignated surface manually. The manual process is usually laborious andinefficient.

In preferred embodiments, the OCT image is displayed on the display of awork station as described above. At block 110, a user at the workstation determines the retinal layer to segment. At block 115, the userdraws a small edge that corresponds to the designated retinal surface inorder to begin the segmentation process. This user input associates thedesignated retinal segmentation surface (e.g., outer border of the innerplexiform layer) with the axial locations at a group of the adjacentA-scans. This is shown in the highlighted portion of FIG. 3 . In someembodiments, the drawn edge covers a distance of from 5 to 100 columnsof the image, preferably 10 to 50 columns of the image, and mostpreferably from about 20 to about 30 columns of the image. In preferredembodiments, the work station is configured with software to extract andaverage the intensity of each pixel for a designated distance above andbelow the drawn edge and the pixel intensity is averaged across thecolumns. In some embodiments, from about 5 to about 50 pixels above andbelow the drawn edge are averaged, and preferably from about 10 to about30 pixels above and below the drawn edge are averaged, and mostpreferably about 20 pixels above and below the drawn edge are averaged.This process results in a local reflectivity profile of the designatedsurface and is depicted in block 120. In some embodiments, the axial“window” of the reflectivity profile may expand to cover the entirecolumn, or to cover only a small area of interest either measured bypixels or measured by actual axial depth (in mm).

In one embodiment, the software is configured to utilize the localreflectivity profile and the user-determined location (extracted fromthe previous step) to segment the rest of the OCT dataset. As shown inblock 125, the software is configured to utilize the local reflectivityprofile to identify the potential pixel location of the designatedretinal layer at each column across the OCT retinal image. This isachieved by finding the best fit of the reflectivity profile againsteach column. In preferred embodiments, the software associated with theuser station is configured to utilize a cross-correlation operation orother best-fit algorithm to perform this operation. As shown in block130, once the location of the pixels for the designated retinal layerare identified, the software is configured to run additional edgeprocessing steps to finalize the segmentation of the retinal layer.

At any time during the processing of the retinal OCT image on the workstation as shown in FIG. 1 , the user may optionally tag the image or aportion of the image such a drawn edge or retinal layer with meta data.Examples of such tags include, but are not limited to, name of thesurface, location at the retina, disease indication (e.g. age-relatedmacular degeneration (AMD), diabetic retinopathy, uveitis, glaucoma,central vein occlusion, etc.), and lesion indication (geographicatrophy, retinal detachment, drusen, etc.).

In further embodiments, the processes of the present invention encompassbuilding a machine learning database to provide predictive modeling ofsegmentation of retinal layers in a retinal OCT image. These processesare depicted in blocks 135, 140 and 145 of FIG. 1 . In block 135, an OCTimage and/or associated data such as the reflectivity profile and metadata tags are transmitted to a cloud server. In preferred embodiments,the cloud server comprises a database of OCT retinal images from bothnormal and diseased retinas as shown in block 140. In preferredembodiments, machine learning algorithms are applied to generateprediction algorithms for the automatic segmentation of OCT images,especially those related to diseased retinas. In some embodiments,additional inputs are provided at this stage of the process. Theseinputs include, but are not limited to, expert input as to the exactplacement of the segmentation line correlating to specific morphologicalcomponents of the vitreous, retina and/or choroid and parameter-basedstratification of the segmentation (as in each disease condition, thereflectivity profile may change shape due to disease process). In someembodiments, the algorithms utilize the reflectivity profiles toautomatically identify lesions in one or more of the retinal layers. Itwill be understood that “lesions” refers to alterations in the retinalstructure that are identified in images of diseased retinas but notpresent in images of normal retinas. The algorithms may further provideautomated disease diagnosis and/or severity staging based on dataassociated with the lesion, for example, the reflectivity profileassociated with the lesion, the size of the lesion, the location of thelesion on the retina, and/or the location of the lesion within one ormore retinal layers.

In some embodiments, the work station and/or cloud server include animage server. The image server may include an information storage unitfor short-term storage of images generated by the OCT imaging devices.In addition, the image server may include an archival storage unit,e.g., an optical disc storage and optical disc reader system, forlong-term storage of images generated by the imaging devices.Furthermore, the image server may be configured to retrieve any of theimages stored in the information storage unit and/or the archivalstorage unit, and send such images to any of the workstations or cloudserver to be analyzed according to the embodiments described herein.

The present invention contemplates that a variety of machine learningalgorithms may be applied to the OCT image data. Example data miningtechniques include factor analysis, principal component analysis,correlation analysis, etc. as understood by a person of skill in theart. As a non-limiting example of suitable software, SAS™ EnterpriseMiner™ includes nodes for exploring data and selecting or modifyingcontrol variables as input variables. Examples nodes includetransformation nodes, clustering nodes, association rule nodes, avariable selection node, a descriptive statistics node, a principalcomponents node, etc. The software can further include multiple types ofobjective function models for neural networks (AutoNeural, DMNeural,Neural Network), decision trees (Decision Tree, Gradient Boosting),regression models (Dmine Regression, Least Angle Regressions (LARS),Regression), k-nearest neighbors models (Memory Based Reasoning (MBR)),a partial least squares model (Partial Least Squares), a support vectormachine (Support Vector Machine), an ensemble of models that areintegrated to define an objective function model (Ensemble), etc. Insome preferred embodiments, the software includes neural networkprocedures that can be used to configure, initialize, train, predict,and score a neural network model.

In some preferred embodiments, the machine learning analysis providesmodels and/or algorithms for automated segmenting of retinal layers inan OCT image. In these embodiments, when a surface segmentation iscalled, an algorithm developed by the machine learning process andoptionally utilizing a corresponding group of reflectivity profilessaved in the cloud database is used to generate a disease-specificsegmentation result. These results may then be transmitted to a workstation or to a health care provider. In some further embodiments,software resident at work stations associated with OCT imaging devicesmay be updated with algorithms or models developed by the machinelearning process so that segmentation and disease calls may be automatedat the individual work station level.

In some embodiments, the processes, work stations, and systems describedabove are configured and utilized to monitor changes in a givenpatient's retinal images over time. In some embodiments, processorsassociated with either the work stations or cloud served includesoftware on a non-transitory computer readable medium to automaticallycompare two or more patient retinal images for a given patient that areobtained over a given period of time. For example, the images may beobtained at 1 day, 2 day, 3 day, 4 day, 5 day, 1 week, 2 week, 3, week.4 week, 1 month, 2 month, 3 month, 4 month, 5 month, 6 month, 1 year, 2year, 3 year, 4 year of five year intervals, or for an internal withthese specifically identified periods. Accordingly, in some embodiments,the process comprise obtaining multiple (i.e., two or more) patientretinal images over a defined time period for a given patient, via aprocessor automatically analyzing the multiple retinal images toidentify changes in the retinal images, and displaying an image showingthe identified changes in the patient retinal images. These processesfind particular use in clinical settings, for example, where a patientis being monitored for disease progression of for response to aparticular therapy or therapeutic agent. The processes may also be usedto monitor patients participating in a clinical trial. Accordingly, insome embodiments, the processes of the present invention compriseobtaining multiple patient retinal images over a defined period of timeand analyzing the images to monitor disease progression over time. Infurther embodiments, the processes of the present invention comprisetreating a patient with a therapy or therapeutic agent and thenobtaining multiple retinal images for the patient over a given period oftime to monitor the response of the patient to the therapy ortherapeutic agent. The therapy or therapeutic agent may be approved bythe Federal Drug Administration (FDA) or may be undergoing a clinicaltrial for approval. In some embodiments, the therapeutic agent isselected from the group consisting of a small molecule drug, a biologicdrug, a nucleic acid, and a cell. In some embodiments, the therapeuticagent is delivered to the patient by a method selected from the groupconsisting of topical application to the surface of the eye,subconjunctival injection, systemically (IV, oral, subcutaneous,intramuscular), electrophoresis, intravitreal injection, subretinaldelivery, and suprachoroidal delivery. In some embodiments, thetherapeutic agent is soluble, insoluble in a suspension, of incorporatedinto a biomaterial platform.

It will be appreciated that the invention also applies to computerprograms, particularly computer programs on or in a carrier, adapted toput the invention into practice. The program may be in the form of asource code, an object code, a code intermediate source and object codesuch as in a partially compiled form, or in any other form suitable foruse in the implementation of the method according to the invention. Itwill also be appreciated that such a program may have many differentarchitectural designs. For example, a program code implementing thefunctionality of the method or system according to the invention may besub-divided into one or more sub-routines. Many different ways ofdistributing the functionality among these sub-routines will be apparentto the skilled person. The sub-routines may be stored together in oneexecutable file to form a self-contained program. Such an executablefile may comprise computer-executable instructions, for example,processor instructions and/or interpreter instructions (e.g. Javainterpreter instructions). Alternatively, one or more or all of thesub-routines may be stored in at least one external library file andlinked with a main program either statically or dynamically, e.g. atrun-time. The main program contains at least one call to at least one ofthe sub-routines. The sub-routines may also comprise function calls toeach other. An embodiment relating to a computer program productcomprises computer-executable instructions corresponding to eachprocessing step of at least one of the methods set forth herein. Theseinstructions may be sub-divided into sub-routines and/or stored in oneor more files that may be linked statically or dynamically. Anotherembodiment relating to a computer program product comprisescomputer-executable instructions corresponding to each means of at leastone of the systems and/or products set forth herein. These instructionsmay be sub-divided into sub-routines and/or stored in one or more filesthat may be linked statically or dynamically.

The carrier of a computer program may be any entity or device capable ofcarrying the program. For example, the carrier may include a storagemedium, such as a ROM, for example, a CD ROM or a semiconductor ROM, ora magnetic recording medium, for example, a floppy disc or a hard disk.Furthermore, the carrier may be a transmissible carrier such as anelectric or optical signal, which may be conveyed via electric oroptical cable or by radio or other means. When the program is embodiedin such a signal, the carrier may be constituted by such a cable orother device or means. Alternatively, the carrier may be an integratedcircuit in which the program is embedded, the integrated circuit beingadapted to perform, or used in the performance of, the relevant method.

It should be noted that the above-mentioned embodiments illustraterather than limit the invention, and that those skilled in the art willbe able to design many alternative embodiments without departing fromthe scope of the appended claims. In the claims, any reference signsplaced between parentheses shall not be construed as limiting the claim.Use of the verb “comprise” and its conjugations does not exclude thepresence of elements or steps other than those stated in a claim. Thearticle “a” or “an” preceding an element does not exclude the presenceof a plurality of such elements. The invention may be implemented bymeans of hardware comprising several distinct elements, and by means ofa suitably programmed computer. In the device claim enumerating severalmeans, several of these means may be embodied by one and the same itemof hardware. The mere fact that certain measures are recited in mutuallydifferent dependent claims does not indicate that a combination of thesemeasures cannot be used to advantage.

It should be noted that the above-mentioned embodiments have itsapplication beyond the retina imaging. In the field of ophthalmology, itis applicable to be used directly in in the eye for cornea to delineatetear film, epithelium, Bowman's layer, stroma, Descemet's membrane,endothelium and retrocorneal disease processes exemplified by but notlimited to retrocorneal membranes. Additionally, it applies to otherfields utilizing OCT technology to delineate laminated boundries withina structure both biologic and nonbiologic. Examples where OCT is used inmedicine include but are not limited to assessment of the cornea andtear film, the gastrointestinal tract (Tsai, Tsung-Han, James G.Fujimoto, and Hiroshi Mashimo. “Endoscopic optical coherence tomographyfor clinical gastroenterology.” diagnostics 4, no. 2 (2014): 57-93.),Dentistry (Otis, L. L., Everett, M. J., Sathyam, U. S. and COLSTON, B.W., 2000. Optical coherence tomography: A new imaging: Technology fordentistry. The Journal of the American Dental Association, 131(4), pp.511-514.), Respiratory (D'Hooghe, J. N. S., De Bruin, D. M., Wijmans,L., Annema, J. T. and Bonta, P. I., 2015. Bronchial wall thicknessassessed by optical coherence tomography (OCT) before and afterbronchial thermoplasty (BT). European Respiratory Journal, 46(suppl 59),p. OA1763.), other medical fields, and monitoring of biointegration ofimplanted biomaterials.

What is claimed is:
 1. An optical coherence tomography (OCT) imageanalysis process comprising: visualizing an OCT dataset from a scan of apatients retina on a display device, wherein the image displays aplurality of cross-sectional retinal layers of the retina; indicating aportion of an edge of at least one of the retinal layers with a userinput device to provide a designated retinal layer; at a user workstation, calculating a patient reflectivity profile for the designatedretinal layer and using the reflectivity profile to identify potentialretinal locations of the designated retinal layer across the entireimage; transmitting the patient reflectivity profile to a server remotefrom the user work station, wherein the remote server comprises aplurality of OCT datasets from normal and diseased retinas; via aprocessor associated with the remote server, applying one or moremachine learning algorithms to analyze the patient reflectivity profilein relation to the plurality of OCT datasets from normal and diseasedretinas to generate one or more algorithms that automatically segmentretinal layers in an OCT image, automatically identify lesions in one ormore retinal layers, and/or associate a disease with an automatedsegmentation or lesion identification result, wherein the algorithmfacilitates displaying a refined trace of the designated retinal layer.2. The process of claim 1, further comprising the step of displaying animage with a refined trace of the designated retinal layer generated bythe algorithm.
 3. The process of claim 2, further comprising the step oftransmitting the image with a refined trace of the designated retinallayer to a user.
 4. The process of claim 1, wherein the algorithmassociates the reflectivity profile of the designated retinal layer witha disease state of the retina wherein the disease state is selected fromthe group consisting of Age-related macular degeneration, diabeticretinopathy, retinitis pigmentosa, uveitis, central vein occlusion, andother retinal degenerations.
 5. The process of claim 4, furthercomprising the step of using the algorithm to associate a disease stateor normal state with the patient retina.
 6. The process of claim 1,wherein the algorithm identifies lesions in one or more retinal layers.7. The process of claim 6, wherein the type and/or location of thelesion is used to diagnose a disease or the retina and/or designate astage of severity of a disease of the retina.
 8. The process of claim 5,further comprising transmitting information about the disease state ornormal state of the patient retina to a user.
 9. The process of claim 7,further comprising transmitting information about the lesion, diseaseassociated with the lesion, or stage of severity of retinal disease to auser.
 10. The process of claim 1, further comprising the step of taggingthe patient reflectivity profile with an information identification tag,wherein the information identification tag comprises informationselected from the group consisting of name of the surface, location atthe retina, disease indication and lesion indication, retinal locationrelating to known retinal landmark (e.g., fovea), age, gender, race, andanimal species (other than human).
 11. The process of claim 1, whereinthe display device is networked with an SD-OCT device.
 12. The processof claim 11, wherein the process further comprising the step ofutilizing the SD-OCT device to obtain the OCT dataset of the scan of thepatient's retina.
 13. The process of claim 1, wherein the server remotefrom the user work station is a cloud based server.
 14. The process ofclaim 1, wherein the one or more machine learning algorithms areselected from the group exemplified by but not limited to a neuralnetwork, a decision tree, a regression model, a k-nearest neighbormodel, a partial least squares model, a support vector machine and anensemble of the models that are integrated to define an algorithm. 15.The process of claim 1, further comprising obtaining multiple patientretinal images over a defined time period for a given patient, via aprocessor automatically analyzing the multiple retinal images toidentify changes in the retinal images, and displaying an image showingthe identified changes in the patient retinal images.